{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tuned-cnn-timeseries.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/tfbook/blob/master/chapter11/tuned_cnn_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e32d119-933f-4295-86cc-d88115321fec"
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYNhiKDoki0S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "907b942e-ab95-4361-8e57-47688ce35da1"
      },
      "source": [
        "!pip install keras-tuner"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting keras-tuner\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a7/f7/4b41b6832abf4c9bef71a664dc563adb25afc5812831667c6db572b1a261/keras-tuner-1.0.1.tar.gz (54kB)\n",
            "\r\u001b[K     |██████                          | 10kB 28.4MB/s eta 0:00:01\r\u001b[K     |████████████                    | 20kB 6.4MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 30kB 9.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 40kB 5.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 51kB 7.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 5.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.16.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.18.2)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.8.6)\n",
            "Collecting terminaltables\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (4.38.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.21.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (0.22.2.post1)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2019.11.28)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.8)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner) (0.14.1)\n",
            "Building wheels for collected packages: keras-tuner, terminaltables\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.1-cp36-none-any.whl size=73200 sha256=46da9fda2fbf58dab152c6ba2c1d2d551a4b378966cb9f55f84b9c2c931c1efb\n",
            "  Stored in directory: /root/.cache/pip/wheels/b9/cc/62/52716b70dd90f3db12519233c3a93a5360bc672da1a10ded43\n",
            "  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp36-none-any.whl size=15356 sha256=e695ed86a3192b0dd1b05d609cccc7eb7c9913dad904a2d83a6e6162c42bc0f3\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built keras-tuner terminaltables\n",
            "Installing collected packages: terminaltables, colorama, keras-tuner\n",
            "Successfully installed colorama-0.4.3 keras-tuner-1.0.1 terminaltables-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from kerastuner.tuners import RandomSearch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 20\n",
        "slope = 0.09\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVH2XEt4yA4m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "b1324a95-76ff-42c5-b30c-2984ae9f2fbc"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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wt+VNAKY451SuJM6YR/eP4sE9p+d03Sg/QxkyYvnBOZ+/DNkyKNuXwtLe8cmK\nDHpX6tUKFTjn+Mx9u/ELf/9ES9bluD4qjgfOa79H1Ma7XiKEr75uXfkgl1Vvx2wQ6g/6cnHO8c0n\nDmO0YCOja9A1Vj/U7/nyuRLlyNguy1infq+mH1gyh/WqDd2xv9P6kNWG+i05aUC9/bXdeTkSKjmg\nPLjt+H2LkmXjUL9SslyCnfpb2fbi2wCeAHAJY+wYY+y3Glz8XgAHAewH8A8Afr9V6yLOLXzO5xxI\nFvmZ5XAwIogkns/BeW1+aDZE/aqW/megaAVipVB1ZZf3tAN5EjV3lSaazhQxyzFtc5Ct7LJUd0Va\nYZsJQXcoyJLjiwRtmaDtheX6ODFVxUf/Yye+/9IJGBpDRtfq9yFTM2SheMoogsxTdllaji8D/wIx\nuPuNl6+GxoBrN/XGzs/LthfBfXAevCfFYwaCXZZCkGkak/e/rjuHL7zrWvzS1g24bG1XzWNONn0V\n99FIkKUNGl9KtKxkyTl/5wznb1b+zQG8t1VrIc5d/NAlqMe7/uFJvPvG8/DWFItbNE2kXZbEcmQ+\nxNRyGi6utod4+XiQtWqm7YXqOh2bqMz7qDTx/VN1vZpwuqW48DFB5iQcspyJRqgOWTl8HiqOJ4Vc\nTR8ype2FEK2RQ6bLy8jwvxc4ZElXSjhOb792Pe56z1YkiRq9BvehuoAV28PfDe7H8HQVfcpA8pyh\nwXZ9rO3J4bwV7fjsL1yd+pjTuvcDtUPPVRiLBOpS3GW59FZEEPOI79cXVJxzPH5gDNuPpwdlHepD\nRixjxNxDZz5Kli36DEyW7XlzpQrKWKGTwiGr0/9LRRVkzx8NwuQly8Wh0VK9qzSN53MZeq+miMOo\n7YUfG7atNoYFAten0a5AscvScn256xAIBFPG0GqcNfGdlrbLUpYsHS/mkFUbOGT1xFHW0MBY1PbC\nU17r4ekqPnt/sFO0r00RZKHztbY7j0Zk1RYcivPVkWvsM4nn8Zzq1E8QSwGP87oOwUyCK+rUT4KM\nWH4IIXZGbS9a2Bh2tGjhhk9vw093j8zL7ZUsV2aORL+u2TpkLxydBAB89bFD+Pm/e+yM16R+t6Tl\n2dQ+ZGo0wnK9mKtl6Fqs4apKxtBg6pp0yNT7MTQtcIRCJ2w87PmlNoYVwk+ILdn2wvNjayparnS8\n1NsHasuHAsZY0I4j0QBXZU1XDv/lstXy70iQ5VJvU15OEYdiysCmvjZsXtHY4RQzLM06InIxWXor\nIoh5xPfrlyzFl01dQUZz/IhljJoTmqsL1cofJccmKrBdv6Yr+1wpWi5WdmQBAFPhiCE1H1YPVZAN\nh/MTpyoOJstO6vN2aLSEqS5eIgoAACAASURBVHJz46jUH4Npa1EzZMlQv+pqmRqrW4prDzNTYpZl\n2Y6cQkMPBJHt+fibB/biF//+cQBRCVptQCvEVuSQ+bEyqnqeXJfe2CEDoiHg4nEm+cZv3YALV3Uo\nlw9uayaHTC2fiufx7deuB0tOPU8gSpUmOWQEsbAEof70g5F6wEo9XyknEMRyQ/2hMdcfFa0cLi5m\nGKrd58+EQtVFf2fokIUiq5ldlkKQdWQN+VkXT11SkPg+x+1/OYj3fPXpptaklovT1uIoGTLHj5cs\n1e8lU9fQlq11oXrbTBl4F6JoUhGLRhiSt10PB0+X5LxJNUOW3GUZc8g8Hivt1S9Z1g/S54xIkKV9\nlXZk40JTOGTremZwyBRXTjij//XqdQ2vAyglyyWYIVvQTv0EsdB4vH6oWQ7OJYeMOAtR39e256d2\nO5+JVuYoxQxDdWD2mVC0XNmrSois2ZQs+zsy8vF6fiSU1GrcgdPBSJ+Xhibr3p7levjtrz+LD7zx\nEqzpikRF2lrUtiLxDFl8l6WhM7l70NSZXOeH3nIpfvn6TQAigTKhCjJdQ1tGR9FyUbajuY/xDFmi\nZKmrDpmPtoyO6TCfV6/tRbKUqZLP6NIdFBky9TF0JjJfohS5ZqaSpfLCfORnL8OKjkzMaauHWPNS\n3GW59CQiQcwjohdTWulB/PqtJ7gid4AcMmL5EXPI5hjsVx2c+Wa0NL8OWcly0Z03YepMiqyqm97/\nS0Vctrc9E+sLBtT+WBMZs0vXxJuUqhyfqOCRfaN4eO/p2GuQtsEg3vZCyZA5iZKlrqHNDIRLLja8\nO/p35JBFHfoNjWF1Vw6npi2MFi3Ynh/EOFxRHYiEnygBGqJ3mReE+tsVB6smQ9ZEyVIIQvE4gUj0\nMVY76ihraujvyDR03dT1AsFrl2y5UQ/hkJ1TfcgIYikgejGlZRek4KpzsLJFHzJyyIhliDjoAnNv\nfdHKxrDCISvMgyDjnKNouejIGcgZuixZcl6/d5dguuKgMxtcT3zWfZ7uDL4wFOzCXN1V370RwfmT\nU5V4hqzhLst4hiy5y9LUmSxZ1hNkkUNmK9fTAkE2VcVowZa3HWsMK3dZxsWd5QRtL9Rh3bmakqVW\nc90ka7pyMpsnBVko4DoyBrREluvCVR24ZmNP3dtLW0sjQZhkKTtkVLIkzmo8xQVLfme4M+2ybOJg\ndGyijPu2D+N3Xnv+PKyWIOaPWMnyDB2ylmbI5qFkGYgMjo6sgaypx1yiquPV3QUY3L+DrrwJQ2fS\nxRLfDckfY8Ihsxq00xgLBdmJyeqMuyyFGPISfchUoQREpUcAsd2W+RSHLF6yDByygtKjrep4iQxZ\nWLJUHKeMocn1qg7Wqq5sbP0iGJ/sT6aypjuHF8ISryhZBgLOSW1R8bG3XVH3tlTU9Ta6/ySRIFt6\nftTSWxFBzCOiWpHmEDgzZMiEc2a79Q9G920fxqfufWXeyi7EuckH7nkJ33zyyMwXnAWqGJjJJaqH\nEHLzUbY/OVXBQ3ujMWZCuMzHZ6cUCo6gQaoWEzczBfunKg668yYyuiYfZ/RDLnrcns+x51QhvM36\nz4dw/k5OVWIuZWrbC6VEqoq/qhNve2FqDHlZslSFSCTI2kMHbbQQzbo0NIY13XERpTpkPofsW6a6\nTFlDk6eLEh9jwK0X9sduKypZ1he8a7tzGC/ZqDoe/IRD1qir/kyoDllGb/52opLl0nPISJARZzXi\nF1natv2Zdlk245A5SviXIObKo/tP45lD4/N6m/FdlnN1yOavbP+1xw/jd77+rDwoi9mD8yHIioog\nS/brmqn1hRBkhs4iZywlQxa0Dwn+bSni6thEGd974bj8ezzMxp2crM7Y9sJRvmNiPb+qcdfQ1DUp\nuHJ1HDLRzf/UdFWeZmgaVnfGy6tVxwsqBqEgEeXdjB53yITIvXxdF1Z3ZXHP776mpqWE6EPWaMPI\nmrB9xfBUVQpdIf7yZyDItHAsFNB4U0ES2YeMHDKCWFiiLEhaqL/xr/9mAs1RaYOC/8Tc8Xwe67A+\nHzjzUbIUDtk8ZMimKy5sz5cheumQzUPJUnTp78gZNeXJmXZaRoIsmvkY/ZCLPvvqc6C6Xf/8zBD+\n6J4Xo9mM4eMqWK7MkwHpczXrZcimE4LM0DUpXuK5segQLsYkDauCTGdYnditWLbjYf1C1YWpISa2\nsoYu349b+jvw1IffgOs399Ws39AZTJ3FZkQmWRfe/8mpqhS6QsDVa3bbLEKIZWYhrjJLOENGgow4\nq/FTSg+CaEt/uuBqplwjhFijTub3PDuEj35vR3MLJs5JHI83NQh7NtjzGeqfB4esEjYsHSvZ8H0u\nxUqhmt6AdTYUEyXL2P3OomQpHqeXki+N9RRTRF6h6oLz6LKqCDsyVkq9jkB8x/BEe57kRgdTZzLL\nla8T6heCTHXiRKhfRX2uAKBgOUgaTBldQyl8vRqNbDI0bcbdkKJ9xfB0Rf5AFg5Zo2xfM4jrz8Yh\nkyVLcsgIYmERv3TTDkhSTM3Yh6xRyTL+BZ7GI/tGcd+O4eYWTJyTeD5vqonpbJgXh2wWrV8cn+OL\nD+6ve1+lUJCMFS1MVRx4Pse67hx8Hp03V+IZsmTJsglB1mbC0Jj8TkgrWbpKuU19rYQ7J3J6Y0Vb\nlgOPjkdTCJLr8P0gyC/cIjXnV0g6ZFrUh0wVnGrJrytfO4Dc0Bg6skas+WrRCsSeKIEWqm6N6MoY\nGspWsN5G7pehsRkD9aLj/onJas0uyzN1yHJzcMhEyZJmWRLEAiOqDGkHlHq9hgTJnkRpyPBvg5JO\n2XKlO0AQaTiev+RKlupg7GYE2b4JH3/xoz149nB6Fq6sOGRjYc5qy8p2AFGOaa5I1ydn1Dg2jQRZ\n1Qn6cEUly3jbC9UZFM9hZ86M3aYQg+MlG//7/t04MVmRDWrVsVBJwS2+d4TQUnNpyVydaUQly3oO\nWc7Ua9o/iLLcamV3pCzvhiJtOixZqmQNDWUndMga9OsKRjM1FlX5jI6eNjOWIcuE1zmTUD8QBPt1\njc1qx2SGMmQEsTjIDFnKTsmZfv2L6zR0yJoo6ZRtD2Vn5gaVxLmL58+tZOl4fqzFAxD05Prgv76E\nZxRhNJeSpZPiDjVCiJl6bpcQnGMlG+OlQHCctyIUZGcY7Be3nTf1WZUsR6YDYbiyM4uMzmSu1E2J\nOojTuvIGHI9LN02IwX966gi+NHgAB0dLuGxtFxgDhiZmFmRCYIlSY3tGr3HITE2TJcuYCEsIsO6E\nSyZC9+pcSHHbUYbMQSZZsmzSIbvx/BW4/dKVdc8XrOnK4eRUVX4fC0PuTEL9QLoInQlDjk4ih4wg\nFpRGDpbb7C7LZkL9jRwyx2uqQeV88bXHDuHEZGVB7os4czgPSlflObion/zhLlzziZ/ErluyPdzz\n7DH8ZNeIPG0uDlmsXJf4DHz3+WP4wUsnYqeJ6FK9xyEO8GNFSzpiG3oDoXCmwX7x+DKGNqtQ/4mp\n4HOyrjsPQ82Q+bUtcYQ46wx3M1bD+yzIcmkkhlZ2ZtGeMTAR5slMnWGq7MR2QIo1C1Eiepu1ZQ2Z\nIRMulqEzeTlDZzC0IEyfdIaEIBP5fCE6/uhnLsJHf+5yACkZspSSZdbQZYaskXD51ZvOwyffflXd\n8wUb+9qwf6RQU5UQrTzmStbQZj0STPYho079BLGwyJJLygEp2mVZZ3RSnVD/57btw1cfOxS7jUai\nTZQr57sklUah6uDjP9iF7ycOlsTSRfxoaGbuYpKnDwed4+9XMoqi9DVViZyzOQkyt1aMCL72+GH8\nY6JvWiTI0h+HOMCPFW0UwhzT+h4hyM7MIRNrzRqadJw6Q8HRyHk8GQqytT05GDqLdlmKH3Ipz0FX\n2MxUvF6iZKkK0RXtGbRldLmjtCtnYtvuEdz46W01axbOl/jB1pbR5feWuC9TaQxraMFoo7RAvMiR\n9bYFw7ZFufHV5/XhZy5bDSBqqaE6ZGklS+HYzYdwuXFLHw6PlTEUZuoiMXpmtz0Xh8yUJUtyyAhi\nQREW+b3bT+LdX34yVjYUJZZ6o5OslC7l920/ib/+yV78zx/sAtCkQxZ+cc/FAZktYq1zObgTi4Nw\nDeYS6r9mYzcA4F+fOyZPs9za9229kuWO41N1xZoQIMEgaB+nC5YUKkXLrVmv2OBST5CJ9+R4yZaO\nmBBkQqClMVq08Oa/fRgHThfx3eePyYO6inh8gUMWHNa6UnYdJjkxGThW67rz4S7LuCBTS7Xi+Yxu\nN3g8QuCorSqmqw7as4YUVqpoEN9BSYdM3J4awBdunKkztIXCLXDHap1AIHLIVrQHgkx1t8TzIty3\n9kxUKk0TZIJGJctmue2ioKz58N5RANFjb8ucmUOWM2fe5ZmEdlkSxCIhBNlTh8bx2P6xWK8f8eWb\ndrASQ8mBSGxte+UU/uieFwEA/R2Z8LyZG2eKA9FCiCSxVtUVeOLAWOyATSwNqo6HO774GJ44OAYg\nOODPtoGrEAmPHxiTOaw0RyjtdncPT+PnPv8oPv/Tfem3HWYo86aOyYqD1372QVmmLFlujfASd1tv\nA4twyEaLlhQF65soWe48MY3dwwU8c2gc/+Oel/CNJw7XXEaI0IwiVIQ4aSR0T05V0NNmIp/RYWga\nfC4GfadkyJIOmRBkoUMmHtPFqzvwC6/eEAusn5iKSpXiemJMU7sM9Qe3r+a9uvJChCkOma4FJcxG\ngiz8flJ3EgrhksyQAbVd69Uy4Hw4SRev7kB/R1ZOaogGmp9Zhuy8Fe3Y1Nc2q+ss5VmWJMiIsxoh\nwMSXkOoaNJplKYaSq9f5H/e8hC39HfjFV2/AVMWB76uirXGoX/1/KxGPST0IfeeZo/j/tu2Vf5+a\nruJn/vqhVKeBWDj2jxTx0tAk/vTfox51sw32q+7WydDtScsqprlgz4blTuES1VwnfG+3Zw1wHryn\nRBuHsuXV/MAQRlRaqD/YtBC2hSjZKFRdZAwN/R3B7r/Jcn2H7FQoZnYPB2OLjk3U5iNt10dG18BY\nVMpry+gwdVbr5Hk+tr1yCj946QSOT1SkABIHaMfz5feG+rzZiQxZxQ5GAQmhKb5jvvfeW3DeivbY\nDEi1LYNw1KbCx9zXHjwHQqBt7IsEmXTIjEiQmTqDoWmp7SYiQZYNH5MyDkk4ZKGAVNtkJHXRfDtk\njDHcdH4fRovRJgogGKt0JnzkZy/D13/zhlldx6RdlgSxsHz5kYPYMx7NThNZjlhIN3STxK9ilWSY\n13I9TFUcvPWqNbh0bRccj2Oq4sw4oNxX+ksthCATj0M9sLs+h1pR3T9SxL6RIvaEBzhicRDvC9WB\nmK2Lqr7vRIf2NFGXJtL2jxQBAJtXpDsM4rZVp2eyHDRxLdluTQneaZCFUy87XrIxXXXRlTNg6hp6\n2kycLqaLQvVx7ZlJkIUiQoiJrBm4Zcn1/HjnKfzW15/FH3z7BTy457TsJC9Ek9o13/Vrf8Cp2TSx\nYQeInDJxO23Z6Hm79/234Xdfdz6AqLQ5GX4nCbddOGQbe6PXo1NkyDS1ZKnB0NIdMiGy+lNKluJ5\nEU6e+rontV13TKzNj0wQIgwA3nf7hfjcO6/FW65cc0a3qWmNpwSkIT5v1IeMIBaIT/7nK/jM01WZ\n4RBflvESRLzHUKHqyC7bapsM1+Py129nzpRfoKNFS35hJwWdQPzqBYCK0zhDdnSsjL2nzkwkicen\nDj/mPD6WRRywS9QbbVERr4PqYsw2R2a7PnrawpE5YUBdFV/CRUlzyPaNNH6vRYIscnqmKg6qjh8b\nSi0QdyHE1+P7R2VoXoii/o4sJso2Jsu2nL24qjMr51qmIXYmisHexyZqnV3L9aQgEw5ZRg8C/kmB\nKtYkSo9rewJBJh0y108dLi5D/UopVJ05Wag6sZ5YqkN2wcp23LglGD0kvouEQybKi+J129hXK8gM\nXUNH1gBjwWtq6AzZBiVL4TyqPcQYY8gamrz/vvYsesP3TrJk2RsKOmB+HLLgsUQiL2vq+K9Xr6uZ\njbkQkENGEAvEC0cn5K4nIOrUL6g3cNn2fHzs+zvxG197BgBgearD5EtB1pU3sDL8sjtdtGbcqake\ntMS/PZ/jnmeG5DZ3wafvfQV//K8v131sd3zhUfzbDFmwtB17aiYGwII6dkR9xGukHhhm+5rYni9b\nR5ycqnXIMroGU9k9KOCcY/fJQODUa8ciPh9qr6ipii0P6JYiXIDaXZa/+83ncPejwW5kUcbc1JcH\n58DhsbIUGyubFGTix9JE2ZFrkM9DWLIEor5eWUNHR9aQJTrB6aKFjK7hjmvWxy4vXgfH96P2DLFd\nlqFDloscMnUdhaobK/WpJUbGmGyLIRyqyXAXrBBP4nVTM1FCtIq2F//wq1vxC6/eAFPXGmbI+joy\nyJmaXKsgZ0Y9zkydyftKOmR9iiCbLyepU8ms6YsgxATUh4wgFoAjYyX8/N89jv/7n1+UpyWbscac\nr9gOKh9D42XsPjkdZsOiQLPt+nJbfmfWRH9ovY8W7Rl3WVZSBNndjx7CB//tZXz3+eOxyxYsp2aG\nncrLx6dmdNDEutWDsudHmxuC84K1lixyyBaTtFmBc3HI2jIG+jsyUrioAitjaMjoWs1O4uHpqhyC\nXW8HptqGQTBVcWLvG/V9pu7w5ZyjYLkoWvHWEJv7g0awB08XpWOysiOLkQaCTB2WLTieKFvani8z\nUtIhMzSs7MxiJHH90YKN/o4M3n7tOgDAhas6AESvg+txGXVIfkcAkUiqzCDIRGheCD0hjoSrNll2\nYGhM3p54vtcosyeFGyfE5hsuX40VHVm0Z/WaJrDB2oL76Mga+PffvwXvvum82PlZQ5P3b+oaNoWN\neZPaTrTNADCrLviN6FDE4WK2AJPDxZdgH7Iz23NKEEsI8YX5k12nAAAZrTZsb6fsmhLXnSg7sFwf\npwpVeQBrz+oo257ikJnyF+1oIXLI6pUs1bKgEGfbdp8Kbzv+8bMcX+ZIkvjhJoN6TpwgLUPmcx57\nrOKgT60xFhfxnlIdiLlkyNqzBtZ056RDpgoyU9fgGrxGdKk5LKuOCBTvtWSGTH1Pl21Pvo/F3ZZs\nV65B3LZ4z12wskOuMemQcc5TS1inpmvF2vHJMi5Z0yn/Vh0yUabNGhrWdufw3NGJ2HVHixZWdGTx\n6vP68MgHb5etN8QB2vEih0wVsuKz3in7kPmxkmXRcmNiKnLIgtsV7SxeHJrEvz1/HF15IxzZFDxm\n8ZlVdzhGJcv48/LXv3RNzfcHAPSEQqotY+CytV015+dMHcPh+yRjaDgvdMi0xPPeEodMEWSLKYYu\nX9uFK9d3pQraxWbpSUSCmCPJL/O8yZDUSfGSpfLr1+Wyq/aRsbIyZ86A63HZUqAzZ6Anb0LXWJAh\nk6H+5kuWzx0JDhDJ7znL9euXj2RptHFbBHG5SkKQqc+DJTNkJMgWEynI1AzZHEqWpq5hTVdOHmhV\nMW7qgUOWzJCpTmzdWa4pvaICh0zJRSrrFb8lKna0A1O8D4VDtiV0yIDoAL2qMwfL9WtKi0Dwfh8t\nWrLzvBA1yWC/GupXHbLV3TmcmrJiTvlYyZI50I19bdDCD6JpCEEWZS5PFar4+Pd3wnI96a7LTv0J\nh0zcpyDpkAkn7D+3n8QDr5zCM4fHw6Hm8eHiambrqvXdWN+Tjwk9ALh4dacUkipXb+zG773uAtx0\nfl/NeUAgUsXrbWgsajtixb+/1AzZfJX21P5qixnfuvnCfvzwD26bdYf/hWDprYgg5kiyF1g+xf+N\nz6aL/m25HibKQpCVlMaFOhzflwewrrwJTWPo78jgdMGSO8vcOgc19YBVsV1MlGwp3pLrtVyvrlsR\n7fqqvZ8Hdp3C5g/9J05NR8N7K7GSJY9dryozZFSyXEyEyPdT8n3N4rgcGV3Dmu6cLO3FHTKGjJEm\nyKLXXnVldxyfwpOyL5roJF+/ZFlWNqqojWFlr61EXnFTX5v8ISJLlmEEIC1HFjhnwOawtHbx6g5k\nDK2mZYvtqYJMOGQ61nblYHu+zJ8BomSZRRIRbFfbXgzuOY2vPX4Yu08W5I+dTqUPWVKQpWXIMqGg\naQ93XR4Px5oNjVfQk+KQqY7Uqzb04LEPvV46XzORNXR86C2XxgL0KmrfLyHkAWDSir8/+tpa65Al\nHTkigAQZcdaQFCt5o/ZDX7E93L9jOGz8Gh0IR4u2dJEOKw5ZPqOD8yBIDERfKv0d2dAha9yHLOmQ\nvTg0Kf9OOhONHLKoUWXt/dzz7BAA4PkjE/Igqh5kfR5vexG5FuSQLTTHJsr45A93wfejnbuqCJuT\nQ2YEB9bJsoOqExf1ph7M+rMS7zXReqE7b8bO+9sH9uET4RSK6DMQHUgt15fZMyB4Tw9PVfH9l07E\nQv2RIIvnFbuVkn9XQpCNpJQmRS7u8nVd8rIr2jPy86iuS5QsRQPUjBEIVSDa8MA5x1jJkn26VIRT\n6XrRJhjRLqdkufIHVHvWgMZChyyR+cwq6Xixy1I4b0Y4/kiNtfa0ZaTgsVwfjAWtHH7nti1ohWZR\nBWNPmykzfX25uBRQN3Lo81ReVEXifO3cPNsgQUacNSRzXLmUJtAP7jmN3/vH57DzxHTMLVOH/h4Z\nK0UZsvBLdaJkgzGgI6MKMjtyruo4ZKoLVXY8+QUfXCfhkDk+bM+POSbJy6bdj/iiK1TdJh0yUdYk\nh2yh+b1/fA5ffvQQ9o0U0wXZHEL9GV3D6tDpGJ6qNhXqF47vivZMTLxbriedmrQMGYDY4PqJko2b\nPrMNf/jtF1CwhUPmSmEp2r6Ix5XP6FIkqRkyINj9mEQ0or1qfTAiakVHFhlDqyndp5Uss4aGNWHT\nV/H5nqo4cDwuS5YqItTv+L4cLi5eo4LlxkZJ5cP+Zsmyv9oANlmyVB+zQMQfgECQCXH2kbdejkOf\neWvNGs8U8dz0tJnozJnY0t+O79x5E959WX0Hbr4csnjJkgRZGiTIiLOGmQLvAGROTP3FC0Rf2BlD\nw+HRcs3BaKxkoyNryLxJT5sZfrk3dsjEgcnQGCq2F3PMko6eaIOR5pLJ9hop9yO+5KerUaNa1Wnx\nefCfyNGQQ7Z4iJxXztTkzl1VEM2l7UXGYFKUl2y3NkOm5IYEhaoLQ2PozJuJjS5cmYUZOmRmfUH2\n+Z/uj27TjkqW1RqHTIwJMrCqMy7IVjUoWe46MY2MrskeXv0dWZh6rSCzXF+6P8LdyRhRSU44ZKPF\n4POvNikVyLYXrl/zeS5WXfmdYOoa8hkdFSfY7JMJc3oAYnMVRWNYVZB1JIL4sQyZ47U87C7KuWrz\n2ZvOX4FcSjVBMF8ZMlWMkiBLh3ZZEmcNSYcsTZ+pPZRUQSR2cl25rgt7TxVhh33IhCAbL0WNLIEw\nHOv6YCzq7p2GOMCu6MignOhunsz1iL8t14uVDNTHljYIXWx1L1Td2KBqsWtNOG6ez2HojDJki4jI\nMrk+ly7VmThkjhc4ZEKM2Imyt6kzMKSH+jtzBrKGFitxiqkUQOTGJt+LxxVBtvPElPz3dCjILNeP\nPmfKe000NV3THYghISK78yZMnWGkUNveYvvxKVy6thMbeoPs2YaefGyTwreeOoKfvjIC2/WkGMor\nDtnKzix0jeHUdFWWVgFgRXtKyTIUCcFki4QgizlkwQSAquODMQftWR2Ox2OtN4DIXc8ogiaZ7Uru\nsmx193jxHKnjmWZivkSi2ih3MfuQLWXIISPOGpLlvLQ4VkERZKqjdio8GFy7qRdFy5W/qNvCX7Rj\nJTv2C0+4Do1KiUB0gF3Rng2yNTGHLBnq92P/jz+22r5IArHGQtWNrUPcjmiOK/5fJYds0RAvnzr9\nQX1PiNdmpFDFD18+Acfz8Z2nj9Ztq2K7wS5LIcgs10/NkKWF+jtzZmzXHRA4sMKxa6ZkqX6G1Png\nYoOMGupvM3UwxqRrJX5IMMbQnc9Ix1DAOceO41O4cn03VnZm8f333Yq3X7sepqHBDu/3ucMTeOLg\nWCzU39eewZ/fcQXedvU66BrDyo4sTk5V8dH/2IHPbQsGqfd3ppQs5S7LFIfMCj5bGgvcHTEBoOr4\nyJt6zdgm9XmbqWSpZsj0FjcrFevcOIuB3PMlEjXldjRyyFIhQUacNSS/RFMdstCVsF0/Jl5E88hr\nN/UAAPadCub8iR1mEwmHLKMHDWNn7tTvQtcYuvOmnH0n15voiSbWn9aLLAr11297Uag6sedAHAxV\nhyw4vbY1BrGwqNMf1NdBuJb//PQQ3vdPL+CvfrwXH/rudvz7C8dTb0dkp4QzY7m+fH2BINO0piuH\nQ6OlmOtTrLqKQ6YIMteXua9kyVIIjBOT1VjLlrR+TmNhabDiePirH+/BVx49JPNWq7pEyTK6Xj6j\nxdYNBLsQp6surlwX5MeuXN8dPFYlEzdddVC2PViOH8tv/eprNstc3ZruHE5MVmI50fRdlrVtLwSF\nqgvb4zL4nzU1VB0PTripQtx3RilZNsqQifYfPW2Z1AxZqxAZVrVkOROtEInkkKVDgow4a0hmstLy\nVsIVslwvdv6paQsZQ5PNFHcPTwOIejBNlGx05RMOmRs5ZPUcDOEMtGV06ZCJL2VbEXGqS1F1a4WS\nCBknNwIEpwXnFS039hyIA71YWnKsEnXqXzwCh6x2KkPFDl4/sZPxsf2j4eVrhbgfjsQKeo0FQiAo\nWXpyh56pa7j5wn6MlWw5CxIQDpmBrKHHHTLPl4JEiPtcKMREGL/ieLFdiuqQaqEnxPqrjo/7dgwD\nAG4Ic2BvvHw13nv7BbhsbdTYNZ8yBHxHWA4VgX6BaUSjoKYr0aDuen2lLl7dgd3DBZyYrOCajT34\nozdcjBXtaQ6Z6NTv14xcK1oOXC8SfRk9cBZFyTjNIRM/5kzltM5wfNK1G4MffvE+ZF7Ls1WiLNyM\nQ3Z+KBpbIRJb7QQuV0iQEWcNSbGSZibFMmTKF+zwdBV9bRls6M1DY8DTh8bRntFxXniwKVhu7Be9\nKFmKA4NTZ3RS2QryiIovVAAAIABJREFUYPmMHob6XbRnDBgakwdZXykTAekOmXDg0hwyceAMSpZK\nCUmZnRncT3C6EHw0y3JhUQWH7fmpjXnFzlchaMQPg7S+UuK9F3fIgjKacK1MQ8MtF64AEIk7IHCW\nOnNm0BLDrS2j264fZaZCwbCyIysFV19bRv77vBVRs1fRL2s8dMjEe+3NV6zBP995k7zMH7/p0lhD\n3Lyp1zi2h0ZLAKLRRoKMEuqfVhzveoLsinXdGC/ZGC3aeMuVa/D+N1yUOhFAbdCa0GNhqN+XeS8z\nzLHZLpdlYSBRsszWZsjE+KA3X7kGF6xsx6VrOqOh5h5veahfbJzY2Dtzhuzbd96Ev/rFq2ONgecL\ncsjSadmrzxi7mzE2whjboZz254yxlxljLzLGfswYWxeezhhjn2OM7Q/Pv65V6yLOXpIuVWqGrCp2\ntnlwPS4Dy7bro7c9g6yhY11PHj4HrjuvN7bDrEvJf4gvXlEWTHOugKDVRXvWkA5Z2fbQltFh6EwG\nuy/7s/vxNw/slddJDh1XH1ua6yfue7JiJ0qWoeDj8aaycueb7dbM+iRahxpanyzXumPt4XsEiHYD\nyybCKYJfiJKsoWTInMAhE6NvTI1hbXceF6xsxyP7IkFWUEqWar5M3Vjielz22wKCkuVVGwJnpz2r\ny8+G6pD1tAVCUAhKziN3OU0ECXIpguzEZAV97ZmaTQWmEupXc2f1BVk0QkiMbkpDtL1I+/wVLRdO\n6EaK+xI/yNSSpbrLUjw/qsgS7vj1m/uw7QMDWNudjzlQrR54ffMF/QAgO/Q3YnVXDv/t1Rtasg7a\nZZlOK+X41wC8OXHaX3DOX8U5vwbADwH8WXj6WwBcFP53J4AvtXBdxFmKECv/9Ns34pYLV8y4y9L2\n/FgX8t7wYCK6gt+4pS/2K74zscsSiA6Y9UqWFdtF3tTRljFkf6Z8Rpdb9z997yuwXB/feWZIXieZ\npQEi5yKtdCXOmyjFM2TiACfWlgz1c55+X0RrUAdoTyjNVQVdeVO6aOOJ86speT8hSlSHxvaCXZZi\nOLQQENdv7sOO41M4NFrCJ36wC1MVB51ZI3TIaqdXWK4Px/dh6Fqsh9gbLl0FIHBXRcNY1SET9zte\nih7reNlGR7bx3MB8RpeP8ZnD4/jWU0dwYrKCdT25msuaykaEaWXigJohU7lsbZcs4V6wqpEgEz+y\naj8ThaoLx/VlN3/h0jmuj0w4DQGIi0IR/ldLlndcsx4f/tlLpXAVl0v7dyv4zP91FR79f2+PCcfF\ngDr1p9MyQcY5fxjAeOK0aeXPdgDi6HEHgG/wgCcB9DDG1rZqbcTZichZre/NI2foqTsSxUki/6X+\n+l4bNpHcFP7iv2HLitgv1mSGTGVovIzXfvZBOdJl/0gBu05M47kjE1jXk69xyEw9CDH/cyjE1nVH\nB560X+hyIkCDDNlE2Y4JNhnq53HRWE0JkROtR+1EL3YhqnTlzMghKycFWf0ydsbQ5AHWcoIeYG2Z\nILcoclFru/MYK9n4/osncPdjh1C0lF2WiiATt2k5wefD1BhuvmAFVrRn8PsDF+L1lwWCbPdwQYb8\n1/fmpZAQP2pUQcl5VKqrh5oh+8cnj+ATP9iFoYmK/EyqZEMx5Hp+bHSR2nJCpT1rYEt/O0ydNSzV\nic962maXIJ/JpbgSGVIn3N2ZVrIM7luXzhsQhPnvfO0FMbdQddBaHerPmTo2zCLQ3ypa/TiXKwve\nh4wx9ikA7wEwBeD28OT1AIaUix0LTzuZcv07EbhoWL16NQYHB1u5XABAsVhckPshzowdx4LyxTNP\nP4XxcVG+S//g7z1wCGMTHjzlu9edOoXBwUF0VVyszDNMHnwJ+yaig1Vp+BAGB4O36aGheMnpxcOn\nMVbl+LcHHsfVK3Xc+ZMyHB/QGXB73zReOjkO1+c4OjKO3pwG3/VxaOi4FIgjU9Fsvudf2g791Cux\n2989Hix0qlD7XjwyFBzoy7aHHbv3ydOffv5FOMcMFEvBbT/2+BPoz2uYLleR04GqB/z04cewsm1p\nRUnP1s/bY4ej98yuvQcAAIYWlda5XcLpIseDDz6I0URPrp179mLQPhw7baQcXPHAvj14evpAeLv7\nMDbpwc8yvGGjhjXuCAYHBzF5Mrjvn750ILr+iSMoO4FAF893xQqE1CNPPInDRx2Ae9j+7BP4q9tM\njO57QZa4r1mpY7QStL84uOslZDWOsg/YheA3+PBkfNbkqWNHMDh4ou5zMzVuYaIQrOPQ8WDawP6R\nIjbnqjXvhdHTFgolD/dveyh2+tDhQxgcPJZ6+5vzFowuhkcfebjuGophH7Xd+w7UnDc6WUC7X4Jd\n9TE4OIiJsSqmCj6cCkNnhkn3+fjQ4djjfN1ajo0Ybfh+rrqKq10uL8p7f6E/cw8//NDMFzoHWXBB\nxjn/CICPMMb+BMD7AHxslte/C8BdALB161Y+MDAw72tMMjg4iIW4H+LMOPn0UWDHdtx6883YNrYT\n20eH6152zfoNGHIm0GNoODwdHERuvfYyDGzdiAEAHwov131wDHj2SQDAu958q9xKP/HCMWDnS/L2\nXGYAcHD+JZfjqgtWwPnRAwCAP3/7VXjXjZtgPHMU9+zZjmlHx1Xn9WPCnUJHTydwcgQAUFaMqgsu\nvhQD18WzG+b+UeDpp2DmcjXvxQcmtwNHjgIAOleuB/YdBgBcdOnlGHjVOmSffhAol3H9DTfivBXt\n8Lbdh5XdWQyNV3DVdVtx6ZouLCXO1s/b8z/ZC4SCefX6jcCBg+jImTJPtnF1P3adnMYNN98G50c/\nil13zYbNuPvgJN53+4Vyt+L+kSLw8EN41ZVX4L9cuQZ44D5s3LQFz40fx/q1Xfjcu6IoLt8zgq/u\nfAZDZR1AIO6vufxSjBYt/PDgXtx622th6Br4tvsA+Lj62ldjp3MU+fHhmtdix81Bd/pfvusJHCtO\n4q2vvw1f3PEIypMVvPrS8/Hwsb2oJkymqy+/BAM3bqr73Px4Yjv2Tgf39bldjwEIZr7ecOWFGHjt\nBbHLbpvcgR0TJ/GqV98I/PRBefrll1yEgddsTr39W2/z4fP6OTMgjDP89EdYs34TsD8uyjzNRO+K\nXkyjjIGB1+KHp1/CUGUM2ZyB1X1t8H2OnWMjuOziizBw6xZ5vWbexlXHAx64HwDQ09WJgYFbZ77S\nPLNQn7lP54/i7x86cFZ+vueDxfxp/C0A/y3893EAG5XzNoSnEUTTiHKdoTNoGkvdZSkQnfrVppdp\nVr6aIRNiDIBsMyAQLSTKlosjY4E7cPevb8W7woNQdz7I1hQsNyxZsrqNWdMawzoNSpZqfk2dByhK\nQGrJknOOquOjL+xUTs1hFw61NCheG3XTSHfeRMlyZbkvp5TghqcqeHjvaTx3ZKLm9jI6g6ExaCwa\nUJ8sna0OxxWppcTOnBHLngHR+ysI9fupu/46wuyZeB935Q20h2OCetvN2LrldWZRslTnva7rqS0x\nmmEfsulE25BGYstQcnZ1L6NFHfOTFMLRSSJnZqptL5SS5Uz30eh+g3We3aW8d924CQ9/8PaZL3iO\n0vS7hzF2xoVnxthFyp93ANgd/vv7AN4T7ra8CcAU57ymXEkQadiuj3u3n5T5F0Nj0BlDUrqogVmR\nkVGDwBtS8iVmnS/I5BevyKuVbA9Hx4Pt+pv61HYAahNMHYau1c1vWSkHBLnLMkWQqaeNKYIsrTGs\nEHuiD1Oy9xMx//zZf+zA733zubggU4Lygq58kCETokl1LsVpar7QUdpeMMZkT7Gq49eEttd014bj\nxS5LIPgMcc5jzYldjzcUCHlTx4r2LBhjsglq1tBSm652ZmcWZNVwDVNKy/+0DFnG0GB5fk1n/7mI\nIRUhtpIZTkNjsFwfZduV3wdZmSELvkNMucty9mtQv5coW3VuM+O7hzF2M2NsF0LxxBi7mjH2d01c\n79sAngBwCWPsGGPstwD8L8bYDsbYywDeCOD94cXvBXAQwH4A/wDg9+f0aIhzko/8+3b8/reexwtD\nQZnD0LXULzZ1bIn4dat20U47aAkBIxrGCup9+QuHjLG4wFMFmQj1i0ByX6JJZTV1uHj9PmSqQ1aO\njeGpHZ0kBJgQZCUK9becvacKODRakvNRgUiQqQ5tV86A63PZUf76zb01jVZV91S4WmorBsvxYLle\njUvV22bW/LhQe3clR4lVXS9o6VBn5yIAvOmKNfil64PCRocUZLoUZOpHcEaHLKPD8zkcj8ecr/Up\nDllGZ3A8P+akBaef2c5BPXQZkxsoxCDyybIjHXMzXEMwuorVDfU3A2NMfl9RO4hzm2YyZH8D4E0I\nXCxwzl9ijL12pitxzt+ZcvJX6lyWA3hvE2shiBr+9fkgyCvGqRgai81KyxoaGIt/WVquB9ePOwBp\nB5+LVnWgt83Ex992eez0elvsS7aHkUIVa7pyyCXKUYJ8xoCpM0yUgoNyb5sZKyWljk5qMDPTTQgy\nXWPwfK60vYhuQzTq7OsIBRl16285JSsQN6pDVg2FcZsZfQV3he8RMbz7nTdswrtuPA+/8uWn5PtD\nLac5smQZiYGoZBkXJ4wxrOrM4fhkBX/8pkvwyslp3HJhv+yirzaCBRSHrIFA+MWtUcqkI8Uh686b\nmAjzce0zNBcVn5XJsg3b9bGyMwvP51IMqZi6Bs6DdhoqZ+qQAcGPOfEc58xgJ/SqrhxOTlUxUbbl\nYxO90MSPOvEJnIsgAwIh5vqtbwxLLG2aCvVzzocSTf2ozkEsCaqOJ7tqCwEiSpaCnKlDY3HBFR1w\nGn8B9rRl8MKfvbHm9Hpb7Mu2i6NjZWxKjCbpyUcuWFtGh6Ex6U71tWdw4HQpWlta2wsxMzO1MWx0\nIC1bLrJG0BagIvuNhZ36FYdsZXhgKZIgazkly4XleLBdXx7kxWuTUxwy4eAOjQeCbEVHFt35IJMl\nephZro+psgPTYLCEQybcmXC+orifJGu6A0F27cYevPf2CwFAcci8WD5RZCwbOWQqomSZMTT0h2K/\nty0jBVlyqHYSkaUbDt3BP3j9hbjjmvWpjpFYs5iXGbhVfF4EWSYmyPRAkIWicKLkyB9wGUODG0YA\nMoYmv4Pm2t9rY18b9o8UySE7x2nmHTzEGLsZAGeMmYyx/wfAKzNdiSAWgqcPRa3uhNjQNRablfa6\ni1fibVevi7lalvx1G1xOHESapZ5DVrRcHBkvy5FLgpwZBX9FyVK4U2LcjCCt55Q3Q2NYUfoq2R4M\njQWdz8XoJNmpn8vbFs5DoUqCrFXsO1XAWNFC0XJlV3fhFMmSpezmzuSImmMTZegak5MhcqYuXyfL\n8fFrX30an7l3d41DltE1ebk0YbC6K3jN1yplwKxSslRnWlquF4bYmxMIaSVLtUzfMVOGLBOOMJsK\nBFlvWyZ1aDkQ/bAaK1pgLPpxMVd3SsXQmfyMCJEoBJlawhX/L9nBjtPsGZQsAeDVm3qD+ydBdk7T\nzLvn9xCUE9cj2Pl4Dai8SCwRDpwuyn9XnKBcx1jcIXv7tevwiTuujDtkridn0z32oddj2wcGZnW/\n9b54J0o2ThcsbEzs2GSMoSc8wOTNQJAJs6tPEWQZXavTGFa4XCkjojxfHszLtgtD15A3dXk70SzL\nqGTZnTdjB3Bi/vn1rz6Dz/90f+CQuUHJsi3cjSjEshDShs7kv49PVtDblpHNQ9XSt+V6GJ6q4uh4\nOTbLEgjEkMhfpTlkYpfwGnW3sCLI1PFM4m+jaYcsWGPWjDtk0fnNOWQiP1dPjKlrHi3a6MwaMp82\nLyVLTZOfEbEm9flSQ/1A0PQ2NsuyjnM+E9edF4ykOjpenuGSxNnMjCVLzvkogHcvwFoIYtaoZZay\n7aaGY8WYDvULW3TqN3UtNTg8E/W+/I9NROWmJD1tJkYKFtoyRiy71qe4c115M7XthZoTczwfuqbH\nzmvP6hgtigHFTA4zB6Jdlq7P4Ye3nTN1dOYMOduTmH8myjZOTVdRdrxQaPsyM1ZN7LI0NE1xyCox\nEaCKK8sNyp3TVUdpexGJgelKfYfsl6/fiPU9+djOTnWXpaM0KLUcX76XmkGMRsroGvo7hUOWkfcx\nk1jKSUEWlGYbCjJdCDILXXkzKpc2KR4bkVEcsmy4pg190fdD0iEDwl2u8vpzK1leFzpk+0aKM1yS\nOJuZUZAxxr4K1HQQAOf8N1uyIoKYBbHZjWG5DkgXZGr5RczqazYjk6ReVkQEsvvaaw8oIkcWZMii\n++1VSjudOSO1D5KnuBfJkVCux+XBHAjKHjkjGtYsLq46ZJEgI4esFXAebKo4NV0F55AOmRBDyV2W\nhs6kyzRVcXDZ2k55WznlvVZ1PFQcD1MVJ9b2AggEyYmwe36aQ3bpmq6aJsBqydLx4yVL1XmdiY6Y\nQxYIMvG+nqlcCdRmyBoJMjEOaqxkozNnyjLw/JQs46F+IN6fUHxuVYFp6prcUTpXh6zR0HPi3KGZ\nT9sPlX/nAPw8gPozMAhiAVGFStnxZIklbWCvmciQzbSLrBHqFzJjkKFe0XYimQsDgO7wAJXP6MgY\n0f32Kk6C2CmXRBVhyRyZ5/PYkHRD15DL6Kgk2l6oGbKcqaEzZ5JD1iIs1wfnwMmpaARSxfGkOJGN\nYUMxEWTIotdQbYWiliwrdhDan664seHiQODojIZB965c42HeAnUGZmyXpevX7EJuxOquHBiCHx3i\nM9WRM2BobMaWF4DqkDVRsgxdqImSjfNXtsvnbX52WbKapr0r2jNyM4b43KrfJabO5HfMXEWhpjF8\n9Teul3k14txkxncP5/zflP++BeCXAGxt/dIIYmbU3kmqQ6axxiXLqiPaXsztC1S9LbXbuqA3RZCJ\nDFnSIRMH36yhIWfqeHD3CN7/nWhuIBAvzdoJQeb4fuygbWgMeVOrbQzLuTzomrpGDlkLEQd1ITAA\noFh1kTN1MKXXVVtKyRKIv39U10X03pquOFK4qz2wRF6wu605QaZ26k/usnSa2IUseMNlq/GpW/NY\n053Dys4cNBaIwpypN+eQZeKCrNGuTOF0T1YctGUMefut2GUJAG0ZAyvCyRZpDlnW0NDbloGuMXQ2\nKYTTuP2SVbhiXfecr08sf+byDr4IwKr5XghBzAU14G65vvxFb8RKlsH/1V+1wsky5+qQKbeVSxNk\naSXLNkWQKc5DrxBkpo5suJ3+P148IfM0QNIhi5cs/w97bx4lx1mf+z9vbb1MzyZpNNolW7Yly8ar\nbDB4Gds4GJPEJARicsKS5OIsJPnlwCUsJiE3CecScpOb7SYBEgKcJCy5wGULDtgwNt4tr7JkS5at\nfRuNZp/eanl/f1S9b71VXd1dPdOz6vs5R0cz3dXdNV3V/T71fDc3cDLE1bmhM7/zuR2tsnRdLgWs\nqZEgaxdVx4sUlwCoCRcDwETZr8gzNU2KaiHmdY1FenWtrOOQjQWCrOp68tjJKktFJKR3yIKQpR2t\nsizbfsgybZWlpjGsK/jP1Z0z8ZW7r8Pbd25A1tRaClmenqigkDEaXiiJv9P1OAoZQxZKtCOHTK2y\nvGJjD67a1IMVHZa8aJJNeGP9C+94zVp857evr2nyTBCtkKZT/yRjbEL8D+A7AD4897tGEM2J51OJ\nK1gtMWQZ3iZaTszUIVOfK5twZa72HZO3Ba5HzjIii8cKNWSpLL6vKot8JIfMrc0hM7QwcVrXNNn2\ngnMuw6ku55F5nxSybA//6wf7cOtfPIBjo2GFXCkhD3CqYsMytIgYF3lKps4iyfa9qiBTcsjGi+Hx\nGg7GZCV1ie/Kpcv9quuQ2V5QhTyzz8e1561AZ9ZExtCb9iADQtE5XrIbhiuBqPDKW3qYQ5ZwYdQq\napXldVtX4hu/9QZYhqYIslq3XVRZ7ljXVfuEBNECaUKWnZzzLuX/izjnX5+PnSOIZsTzqYT4Utte\nMBbN+xAulH/bzBwyf3ag/3xqc08gHL4c5+K1nejrzGBF3kp2yAxN9pYCoi091NCsmnwN+E1jDS3c\nHzNwyEq2G3FoXI/LxrK+ICOHrB28dGoSALAv+B9InhHq5yBp8hxVZyDqmj9+R5yP0RwypTpYOd+H\npyrQGJT8peTJEI0Q4qY2hyzoQzbLvlivPW8Frtrc23S7XEKD3Hqon62OjIGevAVTcYhng6WHTV7V\nPNSVMYcskkPWhtclCKBBUj9j7KpGD+ScP93+3SGI1nA8LsUHgDBkqdc6ZGLx6cqZOBN0Pp9NI0Yr\nSMDPxiouk8KVAHDL9n48eU9/8Lrhl3ghYwQLio6Xh8JF/au7juJPvvciHvzQzZHQbI1DJkOWYfgr\na/ldxtXHuV7okPkhSxNTVQeexyOOItEaYmapaHkCJDtkAALRFYaWjdgCnzN12K4TySFLCokDfh+u\neC4TIHII07lFwlUSjWsFrXbqr8df/uIVqbZTXeb4lIs46j51ZHT80ms34erNvbPeVyCs4ASiF3Xi\nokl26lfbXrThdQkCaFxl+RcN7uMAbmnzvhBEy7geR9bUImOTgGhSvx5zyLqyhhRkXSmdhCQyhoZJ\n1LYYSEroj2Mpi6cedGnPmJqsktvQm8MLxycAAHtOjNf0IVMR1aLiOU1NQ9bwc8g8HhdkoUPWlTXA\nOTBVdVLnHBG1rAuG0kdClgkOGeAv3uICwdCYzEUSt3VkDEyUnboOmcrwVCUiQoQg686ZiI26q0vo\nkIUhS8aUKuQZOsitooZGX7O+cWK7GQlZGujOmbj2vBVt2Q9VyKoOmfh8iAsc1RVTK6YJYjbUFWSc\n85vnc0cIYiaIq3hL9xOlhfOkOl9ibRJXv2ol1EyawgrEYibCLcKpS2p5EUfsnxBRhYyBjKHhnjsu\nxv996hi2remUjgtj0dBsXJC5QbVoRuaQMeQsLQhZRgWZCHeKKkvAH59Egmzm6ME5J2ZQAk0cMuXY\ny/M1OJdE1WW9thcqw5OVxCavrVxkiFDfZMWR51WHZchJFu1wnVrl0iaCTHUF0xQMtII6UUAVo+pn\nBYi6YgvxHhHLk1RnM2PsUgA74PchAwBwzr80VztFEGkR7pCpM1Td8Es0Oanf/+JUc1TWzUKQiXCP\nCFmu6LBwfKyEFSlaDogFWJ1vmTF0vO/G8/G+G8/HX923X25brLrRKstYIYPtehGHTFRZuh5HRZmL\nGXHIlBL9ybKNQ8N+09J+pUM8kQ4heo+NhQ5ZUnNfwF/IxbE3NK2mKli0voiELOs0IZ6uuhHxZc1A\nkDHG0NeZwdBEWQqyQsbwHTNv5n36ZsMl6xsnx8eT+ttJvMGyQHxnTARVrqoopJAl0S7SVFl+AsDf\nBv9uBvBpAD87x/tFEKlwPQ5dZzKEIDv1K+tIvA+ZumDNRoCIL2LhYKwMRiClccisWC7K267egDdf\nukbef/O21TI3qVhxI7lgyQ4Zi+QQiX2aqoRJ+2qVpd8zKbzq/5UvPInf+fIzqf5ugeN6dYXHuYQ4\nNmoOWbFeyNLQ5DlqGkwe/1CQ6ciZetT5atD9PRPJIfMf05WiqlGlvyuL0xMVWThSyBpKyHL+xcbq\nzsafyTl1yJT3XU17EN8ZE0kOGSX1E20izZn0CwBuBXCKc/4rAC4HQN3riEWB7XGYmpIoHYSAdOUL\nU/wok/qzta7CTBCPFeJHhJnS5JCJhU4str9x01bcde0mef/lG3vwrfe/AYA/o1Md/Gy7SQ6Zpjhk\nmtwnVRg4QZWlqfsD2MVi9vLpKRwcnsYTB0cieVDN+LsfH8DP/t1DqbdfrghBNla0pQCum0OmtL0w\nNcUt08McsngvK3Esk6oIX7OhR/4szqW0FZaC1Z0ZDE2WpVgvZIKQpZe+D9l8ou5Tvs2CTH0+tfBG\nXLiJXoImOWTEHJDmbC5zzj3GmMMY6wIwBGDjHO8XQaTC9Ty/ZUBsZJKe0KlfTeoHZj/7LhRk/v9i\nIU2aYxlH5pA1+DIX+SxF241UViaNTtK1sMpSrbKbroYOmRdUWYqFRoQsf/TSabnNt549gffffEHT\n/QeAIyNFvDw05T/nObwoqXl6R84WcX5fR/0cMl2TOWemroYs/dtu3taHC/ujcw3Febqyw8IJZRQT\nAAxc1Bd5bqD1QpXVnRk8dGA4ErIcnqqA86gomWue+NitqS6QIm0v2hyyFDM5AUBXhN8VG3vw13dd\ngVu2+z3RI1WW5JARbaJR24v/A+DLAJ5gjPUA+ByApwBMAXh0fnaPIBrjuFz2cAJCp0HVBzWCLFiw\nxBDkmRIPWV64uhOvPW8FrklR8SUHQtfJDwL8hZgx322JhiyT217I8JeuyZDXtBqy9PxO/eI9EsL0\nvheHUMgYOL+vA/e/eDq1IPMbz/pDns/l3DP12LxwYhy/8I+PYMvKDmjML/RgjEnnTO01FjlmgUB/\n13Vbap5fnF+9CYLsRkWQzdgh68pisuxgMtjHQibsTzdfVZZiP9IQbXvRZodMySHTY5Wqd16xXv5M\nSf3EXNDobN4P4M8BrAMwDV+c3Qagi3P+/DzsG0E0xfE4TF2TLoXMIVOu7OOd+kWoblVhdmNOxAIo\n3KjevImv/vp1qR6bNBMvDmMMeVPHdMWVDV0BRMKX/u9+8rXYn4hDFhNkam+prpwJjfnjfa7a3Iu+\nQgaPvjKcav+BMBx6ZrJyTgsytcbigX1nUKy62HtyAh2Wju6cCV1XBJmu5JCpDlkD4RMPiTPmH2Pb\n5ehThlGHOWStO2QAcGLMz4HrzBpyfxdjyFIVQ+q4qXbQkUlue1GzD5FO/YvvPSKWJo3aXvw1gL9m\njG0GcBeAzwPIAfgyY6zEOX95nvaRIOrieHGHTIQuw23Eha7YRlQprmybQxZtf5EGsQA3C5vmMwZK\ntgM36MbveDyS1M8595P6NS3itghxNl0JQ2d+Un9YOZc1dXzmXTvx3edP4K1Xrsfjr47gzFQFnPNU\nfaxEWG5osoxzOa3U41wex8dePStvz1k6VhQsZA1dtsRQW12YOlPatDQIXQfnlegWnzV0PPChgZrt\nxPGfiUMGAMd7jgkIAAAgAElEQVSDooSevCldv/kMWaZF05j8LKgCqh3Uq7KMo4owyiEj2kXTM4lz\nfphz/mec8ysBvBPAWwG8NOd7RhApEELFjIV+Io1hg9s29ObQYem4JJg5d7tS1TgT4kn9mQbhx5rH\nKmOcGpG3dL/thcul66WGLB0vdAbjbS8AvyBAIEOWykJz245+/PVdV+LmbauxujMD2+UYK6abbykS\n14eUIejnIm7gUK7vyeHsdFXenrN0fPptl+MTP3OJvC2S1K8rIrqBy3LB6gL+58+/Bm+5bJ183tVd\n2ZoQnxDhaedYCoRDdnxMCLLQOV6s7o/4vLc7ZKlWbeoN/nZD16DFLvQIYrY0PZsZYwaAN8N3yW4F\nMAjgj+Z0rwgiJXaQQ6ZBhH5qHQeRC3LzttV46g9uQ9bU8cQ9tzYtr2+GHOos+pE1aE8QJ2kESxK5\nIGTpcY6spWOy4kQS/KWToWtKUn+YQzalOmRByLJeAv7qLn9hHpqsRIZb10OIvaFJEmSaxrCuJ4dX\nh6fl7TlTx451Xag44TGIdOpXQpaNwmOMMbzz2k3YdWhEPm8SIqS5tru181oKstESDI1FEuUXa36U\nP7bMbcv8ShW1r1k8hyyOqfuj0xbre0QsPRol9d8G3xG7A8ATAL4C4G7O+XS9xxDEfON6PLJAhTlk\n4TYi/MZY2J9rtmIM8B0xXRl/U6+jehJpcsgAf4Eo2Q50TZOCL9oCwwueL3TIdI3JZqJqDpnjcVkA\nkERfQQiyMrat6Wz6N5SUHLJzGY/7FwXxqQ/ivIxX5MkB1RqLtMBohhDc9YT/Jeu6ce/v3YBt/c2P\nnUpvMJx7uuoia2qxbvWLU2yYur+faUdEpUX92xuJZCCcZUuCjGgXjRyyjwL4dwAf5JyPztP+EERL\niBwy2e6iQciy3WSCJp+igKAVQSbm3zUTZB0ZP8E6b4ULfNWJdt8HEGkMa+osrLJMaHtRb/EXIbC0\nAqsYySE7d3E9Dp0xrO+NCjJxPjDmi+Wq40Ubwyohy0bhMUEmRa7i9jWNu9wnoWkMfYUMToyXpdAR\nLNaQZUZ5H9tJRIw2E2R6+HkjiHbQKKn/lvncEYKYCU4wNijMy6mtWmsWepgpV27qwZGRonzNlkKW\nWrocspypY2jCHyItFngnoQVG1CFTGsNWoo1hGw2MFqGrtCFIUWV5rocsPe6LLjGGq8PSMV11o932\ndV+QxSsrDcUta4Y4V+qFLGdDX1c2UZAtxqR+wP+ct/J5S4sarm12IScEdbtdOuLcpb0ZkQQxz4ix\nQSJskOSQsTlaU+68Yj3uvGI9zk5V8L4bzsMFfYXmDwqQOWRpkvptBwXPCAWZW+uQ6ZoWdcjE6CTV\nIeN+p/56YaiOjIG8padK0nc9Lp26cz2p3/M4dA3YGDhkV27qxUMHhiP5SBlTw2RFODtBuFppgaGn\nED5ZmavYfkEmxLipR3PI5rMPWSuYSq+9dqJWWTYTWmpPOYJoByTIiCWNI1o+yDmOUWEGzJ1DJlhZ\nyOCet+xo6TEi3GHpjReVfMZAKRguLpyLaJVlkEOmhCz1YNi6xoCimkPmipBl/fdjdWcGZ6aaCyzR\n8sLUWUutMpYjLvdDltdsWYFPv+0yZC0dDx0YjggnebyVUJt6IZFmYZ9Lh0wIMkNbGiFLy9DaPlhc\nPG9aTGWGLkG0AzqbiCWN40ZHJxmxXDIg6pYtFgxlgW6EaAwrhJSusUgfMkcJWYqkbzMIo+RMP3Qm\n8HjjkCXgFzsMTTTPCRMVln2FDKqOh2psnNO5hBdUWWoawzuu2SirHFXhJCcz6NG2F/7IK01W6jZC\nHN+5cIZEY1/L0CLNVhdryPL6C1bhhgv7mm84h1iGTj3IiLZCDhmxpHHiIUvRRkCdZbkIvzOFcEzT\nh6xku/4AcT1siClwlLYXGhPhS/+5c5Yeq7L0YHseCmb9j31fZwZ7T0403X9RYdmZNYHxMhyXo80t\noZYMblBlKRAjuRIFmREOFBfn7Offe03N/MokrHlxyFik2epiDVl+9I6LF3oXYCnfOwTRDuhsIpY0\noiln2Epg/kOWM0FdoBuRD1TOdMWV3fiFQ+Z6XDpVhhaGLNVO/NHRSYh06k9ia18HjowUI49LQiT0\niyakjssxXrJx5/95GK+cmWr42OWGqLIUiJFckaT+wN2KVln6/7/hglWp2rCIUPSc5JB1iRyyeMiS\nloh6WIZGTWGJtkJnE7Gk8RvDapEeXOr/wCINWbbgkAHARMmGHlSTijDlZx58BT/7dw/L57Nigixj\naFI4Af5UA99pq/+aV23uhetxPHt0rOF+iecVY3psz8ORs0U8d3QMe080d9iWE5yH47kAv9v7B2+7\nCG+5bK28zTJCV0yOS5qB2Lnrmk0Y2Nb+UJ0QhGaQiyib185Ry5jlgNq2hCDawTkaZCCWC67nwdTD\n5qzCdYgIskW4qJgpc8hEeGqy4sg2CSKR/+hIUW6nhm3VkFgl0rNMDGOv/35ctbkXjAG7Do3iDRes\nqrtdOUjqF4OsHZej6vq3qX3SzgVcLxqyZIzhd269MLKNOipLzSFrlT9566Wz2NP6qA4ZY36l5UTZ\nIYesAR0ZI3LBQxCzhQQZsaQRjWHDthf+/8IVW4RaDADQnTeRNTWs68413C7eqDJjaJgs++HEsu0p\n96mjk8IFXwgnwBevft+2+otsV9bEtv5O7Do80nC/wpBl4JC5HqoOlz+fS7icN3VhI0n94vgsopNz\nZUcGGgtzxjoyBgmyJnzkzdtRsc+tc52YW0iQEUsakRMVhoSijWHnqkv/bOnKmnjinjeis0kmfC7S\nqFLDZRu68eShEXDOI2IrOlw8fC8iDhn3Q7zNErWv3tyLbz97osb5URG5a13ZIIfM47LS8lyruPQa\nvE8C0Vle08JmsItpLJGuMawqZGqGdi/WpP65RGN+s99mbG2h7yBBpGHOvhEYY59njA0xxl5Qbvtz\nxthLjLHnGWPfZIz1KPd9lDF2gDG2jzH2prnaL2J54QaNTuONYXXpkC3eBaUrazbt3ZVXErhNneGm\ni/pweqKC/aenImLLHy4ezSGLuxuu58Hx6o9OEly9uReTFQf7T0/W3aaU6JAFguxcC1ny5oJMTQCP\nJ/UvFi7sL6AvqLYUzWHTzNhcbjz2sVtx3wduXOjdIM5B5tIh+wKAvwPwJeW2HwL4KOfcYYz9Gfx5\nmR9mjO0AcBeASwCsA3AfY+wizjkF6ImGOJ4/OsmMLXYib2wxC7I0xIcd33iRn9D94P4zEYdM1/xZ\nitvXdGL7Wn+4dDw/zW0yOkmwc/MKAMCuw6O4eG3ybMSibHshmtUqgmyGDtmXnzgCx/WwcUaPXjhc\nL13IMnQwZ55DNpf84y9fLYXlueyQre7Mpqp6JYh2M2ffCJzzBwGMxG77Aedc1NM/BmBD8POdAL7C\nOa9wzg8COADg2rnaN2J54HkcHvfFSCYWBkpqELsUWdMdLgyGxrC2O4etfR14/ODZmpBlV9bEvb93\noxwwXeuQcdiu11QIbFyRQ19nBk8dqp9HVpqDpP5vPH0MX3/6+Iweu5Bw3jxXcUNPTuYL1nMwF5rO\nrClHB4n/z0VBRhALxUJ+I/wqgO8HP68HcFS571hwG0HURTRFNXUNphEVYMKxWOIGGVZ2WLL1hShY\nWNOdxVjRjib1JyycakhM15jvkKXId2KMYefmXuw6PFp3m1LVhcbCPmmOFzpkM03qn664EZHZiC8/\ncQSfeeCVGb1Ou2mUayf4nVsvxNd/8/UAoLS9WLwnZyFz7oYsCWKhWJCkfsbYPQAcAP82g8feDeBu\nAOjv78fg4GB7dy6BqampeXkdojUqQT+uwwdfxXTOXzgO7N+HwalXcLbkiwLPdZb8sVtheShWgePH\njmBw8BSmx8sYKfuDwgXPPPUUzuyPLp6jZ8MRSDo4zgyPoGq7OHn8GAYHhxq+Zpdt49hoFd/9wY9R\nsGqFw/5XK7A0YO/u5wEATz71DI5P+e/5KwcPY3DwVMt/59nxIjwOTE15TY/Zv+0qY7LKsY0fbbgd\nAHxtXxVDRQ+/feXchKFGRksAkPo8O3jEBhCeq4uRsbP+PNPHHn0YOSOdcKTvyaULHbvFwbwLMsbY\newH8NIBbOediRTkORFJHNgS31cA5/yyAzwLAzp07+cDAwJztq2BwcBDz8TpEa0yUbeCHP8BFF16A\njSvywHNP4dJLdmDg8nX+PMYH7kfWspb8sdt+eBeOvXgaW8/bgoGBi/D1k89g/Pg4XMcD4IuB6153\nbU3V17dPP4snTvkfo4xloLu3G97IWZy/ZTMGBrY1fM3xnuP46r5nsf2Ka3DB6tpqsv8aeR6dI0PY\nefWVwJOP4tLXXA7j1ASw90WsWbcBAwOXtPx3eg/fBwAoFIymx+xzBx6DM1XFwEDz5Ot/PfwkxqeL\nGBi4qeV9Gi/Z+Ng3duOTP3cpevJW4jZ/9+IjsAwNAwOvS/WcJx4/Auzdjcsu3YGBy9a1vE/zwSPF\nFzF49FXcfNONqScD0Pfk0oWO3eJgXv1oxtjtAH4fwM9yzovKXd8GcBdjLMMYOw/AhQCemM99I5Ye\nrjJYW7a9iCX1N6tiXAqs7/GdHVE5mjd1FKsOKk40hyyOGhKzdA224+fcpQmViQ7846Vq4v3Fqou8\npcvXtb1wwLio/vQ8jvCaqznFioNyykabjssjMz0bYbscbspt4+w5Po7v7T6JF47Xnz6QpspSZbEm\n9aus6LCQMbRFvY8EsdyYM4eMMfZlAAMAVjHGjgH4BPyqygyAHwYL5WOc89/gnO9hjH0NwF74ocz3\nU4XlucczR0YxNFnBmy5Zk2p7sSDrygiTeNuL5bCerOvxk8FHi36oK2fpKFXdSK+kpJ5W6mJqGZoU\ncGkWWeEGjZfsxPuLVRc5U5fP5bi8pu3FXZ99DNec14sPvWl709fzPI6i7aaeO+p46UWW63G4LQhD\nFREWbvR4L0WVpcpibXuh8suv24zrL1i15ItiCGIpMWeCjHP+zoSb/7nB9p8E8Mm52h9i8fNPDx3E\n3hMTLQgyf+FXh4vLhrB6VJgtZYQgOzHmhyfzlo6S7ULVCEkOmSq81DFKaeYTCodsrBgKMs/jYMx3\nHcu2i5yly/fbcWuT+vednkRfMJKnGWXH/3scns75cjwuj38zbNeT8z9bxfW8yP9JeCmqLFXU0VaL\nlULGwKXruxd6NwjinGLxfiMQ5xxVx0MlZZUdALnIGhrDhasLuHbLCtk3S2fLKGTZ6wuyiXLgkJk6\n7FjILklkqX3ITJ3JCsY0HeJ7ZMjSf03OOa7/sx/hK0/6SfRhyNJ/rqrrSSFWdTy4HsdE2U49Wma6\nEh73NFFL1/NkyLr5tjMPWdrBazQSdGmqLFVEWL3RCCuCIM49aHQSsWiwXS/Sfb4ZYpE1dIbeDgtf\n+43r5H36MulDBgCXb+jBr994Pt557SYA0XFKgqTFXQ2JmbqGYtWuub0eXYpDtvfEBM7v68CJ8TJe\nGZoC4Auy3rwln0sNWdquh8myDc4RyXNrRKmqCrIUDpkbrTJtuK2XPt/slTNTGJ2uYueWFfJ1ADQU\ndF6KWZYq+hIIWRIEMf/QJRqxaHBc3pIgEyErPUGMhP3I2rNvC4muMXz0jouxZVUHgLBpZ2SbxD5k\n8RwyEbJs/rHXNYbOrIFvP3cCd/zNT/Bvjx8BAExV/L7OparjO2Qih0xJ6q+6nsx3S+2QVR35czXF\nQ1pxvRzPaxhyVPmr+17GR76xO/JY///2OWQbevMwdRZp+ksQBEEOGbFoUMfvpEEskknhOjnLcjko\nshh5xSFb3ZnB0GRFFjWoxHPIwpBluvekO2fi4PA0AODlYK7lZCDIRMhShN9sRUxXHQ9jRb86M61D\nVlQEWSXFQxyPw0nZgLaVisypsh1pTitCll6DpH6X85bOsx3ruvDiH9++qIaLEwSx8JAgIxYNtuu7\nLJ6XboFTc8jiaNrySeqPo4YsP3bHxbjzinWJuXJmrO2FEExpQ2U9eRPHgqanhaAj/7R0yERSv6iy\njM6yHAtyz9I6ntEcsjRJ/V5qkeV4HF7KbYtVN+K8CdHXKIfM83jL5xmJMYIg4tC3ArFoEAts2uHU\njpJDloTOlv5w8SRySqPOrKnVLVyIOGSGFubcpUwmF5WWAGSLjalyIMjswCETOWRetO3FeBCyTDsK\nqVWHzG3B9XJbyCEr2a50xQCl7UXDHLLlERonCGJhIUFGLBrEQpg278htkEMG+HMsl3vIMtOgi3ok\nhyyS4J/SIcuFnenHggaxUxUHVcd3p9Q+ZLbLpZC2XTVkOVcOWas5ZCkFWdWN5JtJh6xJDtlyPM8I\ngphfSJARiwbRNqHipu/WDoRtBOJobHk6F2rIMmPU/whbsRwyQVqHrEtxyCaCEORk2ZEVkTnLkOHi\nSMjSaS1keXSkiDNTFfl7urYXviBLMwlA5JCl2bZYdSPhybDKslEfstZDlgRBEHFIkBGLBuFGpHXI\nZKf+OqpLZ8uj7UUctcqy0ZxBUxkKrfYkS5vU35MPBZloEDtVcVC0nWA/dPn+2pHGsFxuH+8rN1G2\n8U8/eRVDk2Vs+cj3cN/e0/i5v38Yf3bvS3KbSkqHTP0/zbZpTLKS7cJWxJctG8O2r8qSIAgiCUrq\nJxYNMmSZMszVLIdMW6Y5ZGrIMmukC1nW+7kRag6ZaBA7XXFQDCysvKWDMQZTZ7A9HpllKUKWZcdD\nqeqi6nrozpn4wZ7T+NPvvShF5V/+cD+Gp6LzMtO0vRDi3fU4ms2+DsOOHnSt8calmqT+5sLPa7HK\nkiAIIglyyIhFg9rpPQ2uHJ2UfBprWJ4hy2wsqb8e9URYmtFJQNitH4AMQToex+i0L6BEcYGhaTWj\nk8T2rsfxR9/eg1/7wpMAwtCn2If9QTsNIDxWaUKWjRyysu1GwpPSIWtyWnkel0n94vGq8KuHO4Mq\nS4IgiDgkyIhFg1g40/auEo5avXCRprFlGUqKOGQN7CGrTlJ/2pYLqwrhHMoJZcj4mclKsB9G8Hws\nktTv9yELt391eAonx8sA/Bw0IGwEqwoqMdA8TVK/EEjx8UlTFQc7//Q+/HDvaXlb6HI1VmRl5bxz\nY4KvsUO2PIU/QRDzCwkyYkHYe2KipjeU7YQhrzS4zUKWWB6zLOOYuiYrJRsl9Rux0Uny9pTqYWBb\nH/7lvddg88p85JgMBYIsZ4VDsh0v6pCNKwLu7HRVPn6yHIY+4xSrjj9RoIke58oAcjsmskanq5iq\nOLJ/GqCItyZJZEXFmosLsYZtL6jKkiCINkCCjJh3njs6hjv+5if4xwdfidwuFteWc8jqhSzZ8mwM\nC4ThwoZJ/YEI01g0HyxtUr+ha7h5++qaPDXhkOXMwCHTWGSWpeNxnJ2qyEKCkemqTO4XDtlUguoq\n2x5ypt7UIVO1UVwoVRRRKEgz/giIztOUgi9NyJKqLAmCaAMkyIh55+y0v6A//upI5HYRgkybQyby\ne+o5PtoyrbIEwnBhOkHGcGF/oeb2tMTz1IYmy8E+6PL51JAlAEyUHfR3+SHP8ZItw4GTlfoOGeAL\nTaHVzkxW8IffeqEmhB0VW1GhJBrRinPI87gUcM0cspJSERrv0E9VlgRBzDUkyIh5R4iJKWVR5jxs\n9Jk2h6xZ2wuN+c1hlyO5oEt+IyEgcsg0jeHC/k55e9qQpSDefHZI5pAJQcZkyFJ9v/s7/eHZnPti\n2/V4mEOmHPu+zjBXLWeFDtl9L57Glx49jD0nJiKvr4qjeA6ZOHeqCQ1dmzlkySHL5u4aVVkSBNEO\nSJAR844Y1CzG8ACIjKtJ36k/aAxbx/FZzg5ZztQbtrwAwj5kOmPYvCIf3t6yQ1YnZBkIMkPXZB+y\ngtIjrb87G3lcxXExERzzSUWQrevJRV5LtL04fLYYeT2BKo5EmJtzjqePjMpzp6q0uhA0m2cZCVm6\nImTZvDEsVVkSBNEOSJAtAn68bwjPHBld6N2YN0Q4SXXI1DBUq7MsGzaGXaYLZd7SkWnQ8gIIhZeu\nsUhlZdocMkHWiIcsY1WWGpPjlApZRZB1RgVZ2fYSk/pXd2bwnd++Hg98aAA5U5MO2ZGRaQC1gizi\nkAU/P/LKWfz83z+C54+PA0Akn03QNIfMDvdJOmOK0zZdcbDr0AgmynbkcVRlSRBEO6DGsIuAX/kX\nv0fToU+9ZYH3ZH5IEmROxCFLOzqpcQ5ZwWLoVrrNLydylo5ME4dMhCzjmjTt6CSBcMjylo5i1cWZ\nST9hXwhhU9dkuK+QUQRZVybyPGXbTQxZru7M4DUbuuXfNRFEKOs7ZOqsSf+8GQ0a0Z4c86srZTK+\nq4q3xkK/mOSQiYH3jocbP/1jnJ2u4jcHtuLDt28HELpuFLIkCGK2kENGzDsiDBRxyJTFsl1tL37r\niiw+8TOXzHQ3FzVdWROd2cbXU+J9EcLpuvNXAoj2J0uDSOrvzpnyuXJKGNPQGaYDMdOhCLI1NSFL\nT4appyKCLNzOr7L0Q5BHhCCbigkyddakqMwNQpWjQf8z2YLDq18AECdaZRl1yKYqDs4GDXHVnmxu\nEH5frk4sQRDzBzlki4hzpZ9RNRgeroae1JBlxfGw//QkPv7/XsC/vPeayCKv0qztRZfFIu0elhMf\n/KmLIqImCRmyDMTC596zE88cGW3ZNRROXNbUUcgYGC/Zkea0pqZJh0oVif1dUUE2XXFkJeN0UEp5\nzx0X46cvXyu3yZo6KsEsTJFn1ihkGTYTFoLM3w87oTrSadJOI1JlKZL6g8eoYk2cqy+dmsChYT+s\nei58bgmCmFtIkC0ihiYrNa5Cq5wcL6GvkEndjX0hUNtacM7BGIssllXHw+MHR/DEwREcHJ7Gpeu7\nE59HuBfLNXG/Eef3FZpuY8qQpf/+FDIGbriwr+XXEg5ZxtCwcUUO48dtmdAP+A6ZCPd1KEn9qzuj\nIUvV6RIhy/e8fktk8HlXzkTR4Tg84rtjusYwHHfIEnLIRLsLIchkDplbu209GoUsy05tmPQzD7yK\nB/afkftJEAQxGxbvqr2E+f7uk/LKuRWOjRZn9bpnJiu47n/+CH/+g32zep65Rm38KVyeasQhczEy\nVRseihM6ZLQYJmHJpP7ZPY/IIcuYOm7d3g8AKCvixdA1FINRSCKpv5AxIvlkQNTpEqOTzFi4eWWH\nhakq5Ofn4rWdCQ5ZrTiSDtm0Hfm9paT+BBdMiP6y4p6Jc3Wy7Mi/m0KWBEHMFhJkc8AHvvYc/v2J\nIy0/Th33MhOOBwnNjxw4W3cb1+O494WTkRDhfKM6ZGIBjST1Ox5Gguax4w0EmetxaIzCRfUQYme2\nYkEKMkPDbTt8QXYimE0J+HMyp2NJ/d05s6boQHW6PO4Lxvhoq5UdFjiAPSf8askrN/bizGQlcVi4\n/7OY7uC//pgMWUYFlf+a6UOW8VmWaqGJeO5i1ZHCj/QYQRCzhQRZm+Gco+y4qbvNqxwdmZ1DJhKm\n486EyuMHz+I3/vVp/Pf/eG5Wr6Wy98QEdvzhvTilLNKNUN8b0bU/kkNmezKBOkmQvXJmCvtPT6Lq\neIs6NLvQ6BoDa4NgFfMys6aOS9Z11dxvaJo8ph0ZX4T1dpg1bTniTlfcHQOAFcFA8xdPTqIza8g5\nmpN1KnLjOWRCGCa2vWiSQybcLiDMQQsdslpXbrrqQmg8ClkSBDFbaDVrM47Hg87k6QSZeuXfikNm\nu17N+JmpYCxNoUH1nVhovvXsCRw+23pYNYkjI9MoVl3p0MX5+lPH8DtffkbZB8UhizkagB8SGhEV\nbeVaQfZH396Dj31jN8ZL9rJN2m8HjDGYugatjQ4ZYwzf/Z3rcd8HbpT3q1WuhYx/PHpyVs3g87gg\nsxIGo6/qsAAAe09OoL8rK7v4q4+N5JC50RwyQdIMyqajk6rhORh3yEoJIcui8vkjQUYQxGwhQdZm\nkpKJG6EuLkdbyCH72x8dwNv+4ZHIbWNByX9nA4dMzb95egbNaN/3pV344+/sjdxWlXk8yf3Ddh0e\nweBLQ+H2qkMW5Iqp70PFcaUgS3LIhqeqODNVwVjRRu8y7TPWLkyt8XilNKiCDAAuXd+NC1aHo5jU\nzv+FwCHrzptgjEVEV61DVvv1s6LgC7KR6SpWd2bQFzhme5XxSW5CK4t4qxQhmpKGjNdDbQwr2mXY\nCYJPPKd6QTRb0UsQBEGCrM0khUoaoS4YjfKl4pwcK+HURDREOBK4TfXaRABRoWg79ffx1TNTeMc/\nPlrjUB0anq5x1kRYp17/MNvlcrg0EE3gF3+z7aQPWU6UbIxMVzFarKInZ9X9GwjANLRZd5EXVZb1\nBpmrRRXCne0JnEvVJRM5ZOL5khyylR1hZWZ/VxZXburFRf0FfPQbu3EwSPRv1IdMID6HrThkxaor\n3yunJmSpCrIwZCkgh4wgiNlCgqzNyGTiJlfjcntFFJVSdqgXrxN34UYDEdNocVAXpUqDsOpzx8bw\nxKERvHx6Mvp4zmvEpqx0q7P/tuvJ4dKAv1iKxqKiss2OOGSe/FvGS7W9tsZLNibLDoanKughh6wh\nbQlZGlGHLI6axyfGJYnjkjV1mfAuRi4J0ZXUoFZ1PFd3ZZCzdPzDL1+NqYqDhw4MA0gWWXF3NnTI\nWhsu3pk1g+eNXliJtheWoUmRpuacUZUlQRCzhQRZm6koIcsvPXoIX3zkUMPtVbdIbSXQDNvlNXlq\nI0HFYqP8tchg5gaFB6InkwgpCjyP14hN22vmkEVdhqrroSOjw9QZisFtYpHrsHScmazI/Yy3vXBc\nT7bKODpSQm+eHLJGWLrWtpBlPYdMTc7fGAwxF8clY2hYEfw8WXZQyBiyqWySQ2boGgqBJhPibn0w\nfFycC0mJ+uUUDlma4eJdOd/hE0JOnLvi+fKWjqrLUXW8iNgjPUYQxGwhQdZmqopD9p3nTuB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3IQnNdi7JOhh2JyvGSjWynU0SmHjCCIWUIrWxPWdGcxOj3RdLt4Ppaa957GIctbOnSNRZy1Zo9R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\nSZIkDew/xSJGlKE42+oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  series = tf.expand_dims(series, axis=-1)\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "441mSOFfks4u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "71729af0-06e2-439d-ed81-aaa75c0fb40a"
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "def build_model(hp):\n",
        "  model = tf.keras.models.Sequential()\n",
        "  model.add(tf.keras.layers.Conv1D(filters=hp.Int('units',min_value=128, max_value=256, step=64), \n",
        "                                   kernel_size=hp.Int('kernels', min_value=3, max_value=9, step=3),\n",
        "                                   strides=hp.Int('strides', min_value=1, max_value=3, step=1),\n",
        "                                   padding='causal', activation='relu', input_shape=[None, 1]))\n",
        "  \n",
        "  model.add(tf.keras.layers.Dense(28, input_shape=[window_size], activation='relu'))\n",
        "  model.add(tf.keras.layers.Dense(10, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "  model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(momentum=0.5, lr=1e-5))\n",
        "  return model\n",
        "\n",
        "tuner = RandomSearch(build_model, objective='loss', max_trials=500, executions_per_trial=3, directory='my_dir', project_name='cnn-tune')\n",
        "\n",
        "tuner.search_space_summary()\n",
        "\n",
        "tuner.search(dataset, epochs=100, verbose=2)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 3</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">units (Int)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: None</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-max_value: 256</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-min_value: 128</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-sampling: None</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-step: 64</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">kernels (Int)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: None</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-max_value: 9</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-min_value: 3</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-sampling: None</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-step: 3</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">strides (Int)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: None</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-max_value: 3</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          ],
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: ad567807802377a1fb52c793f8aa3e77</span>"
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              "<span style=\"color:cyan\"> |-Score: 64.34092104645812</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "text": [
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          ],
          "name": "stdout"
        },
        {
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          "data": {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial summary</h1></span>"
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              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Trial ID: 99ed23990c408176e2198bedfea405b5</span>"
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              "<IPython.core.display.HTML object>"
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        {
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 62.30453399865806</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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          "data": {
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          }
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        {
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              "<span style=\"color:blue\"> |-strides: 3</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 192</span>"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
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          ],
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: bacf045d4d68471ff2a000d794911706</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Score: 61.68776659803325</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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        {
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              "<span style=\"color:blue\"> |-strides: 2</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 256</span>"
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              "<IPython.core.display.HTML object>"
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        {
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          "text": [
            "Epoch 1/100\n",
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          ],
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 6f1d57137054eaef6e041da82a58d765</span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Score: 62.272337097375576</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "text": [
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          ],
          "name": "stdout"
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        {
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          "data": {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Trial ID: b116ed27ef9e2462ea00f367462c79e8</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 64.11483261212199</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          }
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              "<span style=\"color:blue\"> |-strides: 3</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 256</span>"
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              "<IPython.core.display.HTML object>"
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        {
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          "text": [
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          ],
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 0543e91568ea3d7503a48d4196e0346e</span>"
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              "<span style=\"color:cyan\"> |-Score: 61.76720985879703</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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        {
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          "text": [
            "Epoch 1/100\n",
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            "Epoch 2/100\n",
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              "<span style=\"color:cyan\"> |-Trial ID: 7802a2b5cf6d41d2c928b8c48f1b135c</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Score: 61.573768937506635</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "text": [
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          ],
          "name": "stdout"
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        {
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          "data": {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Trial ID: 59f1b2a88671d28ce4c11f6516987abd</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 61.62720041080397</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:blue\"> |-strides: 1</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 192</span>"
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        {
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          "text": [
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          ],
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial summary</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 8bfa48d34740e9f4f1b8a3c9a379ae8d</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Score: 61.61638944976184</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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        {
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          "text": [
            "Epoch 1/100\n",
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            "Epoch 2/100\n",
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              "<span style=\"color:cyan\"> |-Trial ID: 1b4f489a2864e3929523d9fe38ca556a</span>"
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              "<span style=\"color:cyan\"> |-Score: 63.48064323477194</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "text": [
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          ],
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 3f8f076c7486c87ecf1f9e1eecbc3e89</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Score: 62.252636106322406</span>"
            ],
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          "text": [
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          ],
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<span style=\"color:cyan\"> |-Trial ID: c09c9b3a9df89adbd468e31309c68860</span>"
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              "<span style=\"color:cyan\"> |-Score: 63.4553330557687</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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          "text": [
            "Epoch 1/100\n",
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              "<span style=\"color:cyan\"> |-Trial ID: de5575b161f18530b444cbb19d7d2956</span>"
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              "<span style=\"color:cyan\"> |-Score: 59.97414476044324</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "text": [
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          ],
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: f3067d8e8c7481b0092af99d92997e76</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 61.44847151568147</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 192</span>"
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          "text": [
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          ],
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 587b9dc8ddc2f561987b849056c2f5d2</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Score: 62.03023592890525</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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          "text": [
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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              "<span style=\"color:cyan\"> |-Score: 62.20527395131637</span>"
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        {
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              "<span style=\"color:cyan\"> |-units: 256</span>"
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        {
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          "text": [
            "Epoch 1/100\n",
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            "Epoch 2/100\n",
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          ],
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: 8a00a67c2e2b42cf0992770ab8c5140f</span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Score: 61.87714039809038</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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          "text": [
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          ],
          "name": "stdout"
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Trial ID: ac839e8a62c421239229187a4fdd69e5</span>"
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            "text/plain": [
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              "<span style=\"color:cyan\"> |-Score: 64.15516894074524</span>"
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            "text/plain": [
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-units: 128</span>"
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          "text": [
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        {
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<span style=\"color:cyan\"> |-Trial ID: 706cd727a91606cd54ba864a5d1dca3a</span>"
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              "<span style=\"color:cyan\"> |-Score: 63.52073707840069</span>"
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              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-kernels: 3</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-strides: 2</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-units: 192</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "31/31 - 1s - loss: 895.3913\n",
            "Epoch 2/100\n",
            "31/31 - 0s - loss: 151.6122\n",
            "Epoch 3/100\n",
            "31/31 - 0s - loss: 100.2846\n",
            "Epoch 4/100\n",
            "31/31 - 0s - loss: 84.0591\n",
            "Epoch 5/100\n",
            "31/31 - 0s - loss: 75.7795\n",
            "Epoch 6/100\n",
            "31/31 - 0s - loss: 72.3609\n",
            "Epoch 7/100\n",
            "31/31 - 0s - loss: 69.7808\n",
            "Epoch 8/100\n",
            "31/31 - 0s - loss: 69.5828\n",
            "Epoch 9/100\n",
            "31/31 - 0s - loss: 67.2998\n",
            "Epoch 10/100\n",
            "31/31 - 0s - loss: 66.4014\n",
            "Epoch 11/100\n",
            "31/31 - 0s - loss: 66.3236\n",
            "Epoch 12/100\n",
            "31/31 - 0s - loss: 64.9357\n",
            "Epoch 13/100\n",
            "31/31 - 0s - loss: 65.4288\n",
            "Epoch 14/100\n",
            "31/31 - 0s - loss: 64.6994\n",
            "Epoch 15/100\n",
            "31/31 - 0s - loss: 64.3430\n",
            "Epoch 16/100\n",
            "31/31 - 0s - loss: 64.8961\n",
            "Epoch 17/100\n",
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            "Epoch 18/100\n",
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            "Epoch 19/100\n",
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            "Epoch 20/100\n",
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            "Epoch 30/100\n",
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            "Epoch 32/100\n",
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            "Epoch 40/100\n",
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            "Epoch 41/100\n",
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            "Epoch 42/100\n",
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            "Epoch 47/100\n",
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            "Epoch 48/100\n",
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            "Epoch 50/100\n",
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            "Epoch 91/100\n",
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            "Epoch 99/100\n",
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            "Epoch 100/100\n",
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            "Epoch 1/100\n",
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            "Epoch 2/100\n",
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          ],
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              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<span style=\"color:cyan\"> |-Trial ID: a5ebc1fb4d2aa8ace4eba9148b78e82f</span>"
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          "data": {
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              "<span style=\"color:cyan\"> |-Score: 60.06481440018634</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          "text": [
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        "\n",
        "model = tf.keras.models.Sequential([\n",
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        "                      input_shape=[None, 1]),\n",
        "    tf.keras.layers.Dense(28, input_shape=[window_size], activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "\n",
        "optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.5)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer)\n",
        "history = model.fit(dataset, epochs=100,  verbose=1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 697.0467\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 139.9011\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 91.7349\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 76.0791\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 70.5412\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 66.6026\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 64.6044\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 64.1560\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 64.0903\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 62.9796\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.2624\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 62.1205\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 62.0448\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.4723\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 63.3360\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 62.5374\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.5854\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.5909\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 61.6934\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.3327\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.5552\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.9634\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.5907\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.1054\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.7173\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.4174\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 60.9907\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.4215\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.5251\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 62.6943\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.0242\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.2664\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 62.3938\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.1688\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.7591\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.0774\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.1858\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.9149\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.3997\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.2955\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.5250\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.0320\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.3977\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.5393\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.4017\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.3201\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.6043\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 60.9328\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.9017\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.8071\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.8832\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.0987\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.4976\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.6648\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.7899\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.2677\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.7515\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.3328\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.0543\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.9874\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.5349\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.3532\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 0s 9ms/step - loss: 59.8809\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.2755\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.7265\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.3910\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.9028\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.1219\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.1354\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.9936\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 59.7607\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.4187\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.6521\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.6360\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 61.0739\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.4447\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.5836\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.9731\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.4902\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 59.6420\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 59.3949\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.7430\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.3316\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.1490\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.4163\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.8818\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.0081\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.2956\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.0720\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.6368\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.0852\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.9905\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.4356\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.3638\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.4804\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 60.0040\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.5065\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 59.5952\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 0s 8ms/step - loss: 60.5840\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 0s 7ms/step - loss: 61.2026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_eaAX9g_jS5W",
        "colab": {}
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKkic-mLdkRZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast = model_forecast(model, series[..., np.newaxis], window_size)\n",
        "results = forecast[split_time - window_size:-1, -1, 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efhco2rYyIFF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "1a605855-2f38-4028-82d8-0e3e51175877"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+/QdpcVsn3Ha+X7f2Vu/vdcfBNu5/sAeAQ8faaWnpnbcxAYwm07iKkufm0GHv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\nEWSCIAiCIAhTjAgyQRAEQRCEKeb/B21HWxsvjErLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kT6j186YO6K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c736a0b7-d0ed-4200-cf00-8d87d6fb1191"
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.4636617"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    }
  ]
}